Parallel Model Predictive Control of Switched Reluctance Machine Without Weighting Factors

Shoujun Song, Chenyi Yang, Minghui Wang, Lefei Ge, Zhaoyang Fu, Weiguo Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

In this paper, a new model predictive control strategy is proposed, featuring a Fourier model based on switched reluctance machine (SRM), cross-selection of the optimal switching signal to drive the machine by reconstructing the torque and current error formulas, considering torque ripple and copper loss requirements, multi-objective equivalent control and elimination of the weighting factor in the cost function. Simulations and experiments of steady-state performance and dynamic performance are also conducted to verify the feasibility of the proposed control strategy.

Original languageEnglish
Title of host publication2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350396867
DOIs
StatePublished - 2023
Event2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023 - Wuhan, China
Duration: 16 Jun 202319 Jun 2023

Publication series

Name2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023

Conference

Conference2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023
Country/TerritoryChina
CityWuhan
Period16/06/2319/06/23

Keywords

  • efficiency
  • model predictive control (MPC)
  • parallel structure
  • Switched reluctance machine (SRM)
  • torque ripple
  • weighting factor

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